VITERBI_UWB

基于matlab的切比雪夫滤波，巴特沃滤波器设计，比较全，很好的源代码( Chebyshev filtering and Buttord filter design baseing on the matlab. more complete, good source code )

三自由度自行车模型，非线性轮胎模型，考虑纵向加速度(Three DOF bicycle model, nonlinear tire model, considering the longitudinal acceleration)

卡尔曼滤波器，扩展卡尔曼滤波器，无味卡尔曼滤波器实现，对比(Kalman filter, extended Kalman filter, unscented Kalman filter implementation, compared to)

Some codes for the solutions of the book Fundamentals of Photonics

小波变换应用MATLAB对于图像处理的压缩程序(Application of MATLAB Wavelet compression for image processing procedures)

高斯核函数。可以用来进行调用，对图像进行平滑滤波，也可以进行多尺度分析(Gaussian kernel. Can be used to make calls, image smoothing filter can also be multi-scale analysis)

Estimates the power spectral densities (PSDs) using the EB method with q = 0 for deciding the diagonal loading.

采用matlab编写的数字图像恢复程序，和数字图像解卷积操作的程序。对常用的几种解卷积方法都有例子。(using Matlab prepared by the digital image recovery procedures, and digital image deconvolution operating procedures. Several pairs of common deconvolution method has examples.)

matlab code of calculated f dos of graphene

The main idea behind this book, and the incentive for writing it, is that strong connections exist between adaptive filtering and signal analysis, to the extent that it is not realistic—at least from an engineering point of view—to separate them. In order to understand adaptive filters well enough to design them properly and apply them successfully, a certain amount of knowledge of the analysis of the signals involved is indispensable. Conversely, several major analysis techniques become really efficient and useful in products only when they are designed and implemented in an adaptive fashion. This book is dedicated to the intricate relationships between these two areas. Moreover, this approach can lead to new ideas and new techniques in either field.